scholarly journals Context-dependent limb movement encoding in neuronal populations of motor cortex

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Wolfgang Omlor ◽  
Anna-Sophia Wahl ◽  
Pia Sipilä ◽  
Henry Lütcke ◽  
Balazs Laurenczy ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Neuronal Activity ◽  
Calcium Imaging ◽  
Limb Movement ◽  
Population Coding ◽  
Motor Output ◽  
Context Dependence ◽  
Joint Movement ◽  
Neuronal Populations ◽  
Layer 2

Abstract Neuronal networks of the mammalian motor cortex (M1) are important for dexterous control of limb joints. Yet it remains unclear how encoding of joint movement in M1 depends on varying environmental contexts. Using calcium imaging we measured neuronal activity in layer 2/3 of the M1 forelimb region while mice grasped regularly or irregularly spaced ladder rungs during locomotion. We found that population coding of forelimb joint movements is sparse and varies according to the flexibility demanded from individual joints in the regular and irregular context, even for equivalent grasping actions across conditions. This context-dependence of M1 encoding emerged during task learning, fostering higher precision of grasping actions, but broke apart upon silencing of projections from secondary motor cortex (M2). These findings suggest that M1 exploits information from M2 to adapt encoding of joint movements to the flexibility demands of distinct familiar contexts, thereby increasing the accuracy of motor output.

scholarly journals Context-dependent limb movement encoding in neuronal populations of motor cortex

2019 ◽  
Author(s):  
Wolfgang Omlor ◽  
Pia Sipilä ◽  
Anna-Sophia Wahl ◽  
Henry Lütcke ◽  
Balazs Laurenczy ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Neuronal Activity ◽  
Calcium Imaging ◽  
Population Coding ◽  
Context Dependence ◽  
Joint Movement ◽  
Joint Processing ◽  
Neuronal Populations ◽  
Layer 2 ◽  
Network Encoding

Neuronal networks of the mammalian motor cortex (M1) are important for dexterous control of limb joints. Yet it remains unclear how encoding of joint movement in M1 networks depends on varying environmental contexts. Using calcium imaging we measured neuronal activity in layer 2/3 of the mouse M1 forelimb region while mice grasped either regularly or irregularly spaced ladder rungs during locomotion. We found that population coding of forelimb joint movements is sparse and varies according to the flexibility demanded from them in the regular and irregular context, even for equivalent grasping actions across conditions. This context-dependence of M1 network encoding emerged during learning of the locomotion task, fostered more precise grasping actions, but broke apart upon silencing of projections from secondary motor cortex (M2). These findings suggest that M2 reconfigures M1 neuronal circuits to adapt joint processing to the flexibility demands in specific familiar contexts, thereby increasing the accuracy of motor output.

scholarly journals Layer-specific integration of locomotion and concurrent wall touching in mouse barrel cortex

2018 ◽  
Author(s):  
Asli Ayaz ◽  
Andreas Stäuble ◽  
Aman B Saleem ◽  
Fritjof Helmchen
Keyword(s):  
Virtual Reality ◽  
Somatosensory Cortex ◽  
Neuronal Activity ◽  
Calcium Imaging ◽  
Barrel Cortex ◽  
Sustained Activity ◽  
Tactile Stimuli ◽  
Layer 2 ◽  
Self Motion ◽  
Increased Activity

During navigation rodents continually sample the environment with their whiskers. How locomotion modulates neuronal activity in somatosensory cortex and how self-motion is integrated with whisker touch remains unclear. Here, we used calcium imaging in mice running in a tactile virtual reality to investigate modulation of neurons in layer 2/3 (L2/3) and L5 of barrel cortex. About a third of neurons in both layers increased activity during running and concomitant whisking, in the absence of touch. Fewer neurons were modulated by whisking alone (<10%). Whereas L5 neurons responded transiently to wall-touching during running, L2/3 neurons showed sustained activity after touch onset. Consistently, neurons encoding running-with-touch were more abundant in L2/3 compared to L5. Few neurons across layers were also sensitive to abrupt perturbations of tactile flow. We propose that L5 neurons mainly report changes in touch conditions whereas L2/3 neurons continually monitor ongoing tactile stimuli during running.

scholarly journals Two-photon imaging of neuronal activity in motor cortex of marmosets during upper-limb movement tasks

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Teppei Ebina ◽  
Yoshito Masamizu ◽  
Yasuhiro R. Tanaka ◽  
Akiya Watakabe ◽  
Reiko Hirakawa ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Neuronal Activity ◽  
Upper Limb ◽  
Limb Movement ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

scholarly journals Stability of motor cortex network states during learning-associated neural reorganizations

2020 ◽  
Vol 124 (5) ◽  
pp. 1327-1342
Author(s):  
Zhengyu Ma ◽  
Haixin Liu ◽  
Takaki Komiyama ◽  
Ralf Wessel
Keyword(s):  
Motor Cortex ◽  
Motor Learning ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Premotor Cortex ◽  
Two Photon ◽  
Layer 2 ◽  
Network States ◽  
The Stability

The neural activity reorganizes throughout motor learning, but how this reorganization impacts the stability of network states is unclear. We used two-photon calcium imaging to investigate how the network states in layer 2/3 and layer 5 of forelimb motor and premotor cortex are modulated by motor learning. We show that motor cortex network states are layer-specific and constant regarding criticality during neural activity reorganization, and suggests that layer-specific constraints could be motivated by different functions.

scholarly journals Population Coding of Self-Motion: Applying Bayesian Analysis to a Population of Visual Interneurons in the Fly

2005 ◽  
Vol 94 (3) ◽  
pp. 2182-2194 ◽  
Author(s):  
Katja Karmeier ◽  
Holger G. Krapp ◽  
Martin Egelhaaf
Keyword(s):  
Bayesian Analysis ◽  
Rotation Axis ◽  
Sensory Information ◽  
Population Coding ◽  
Calliphora Vicina ◽  
Integration Time ◽  
Noise Correlation ◽  
Neuronal Populations ◽  
Response Onset ◽  
Self Motion

Coding of sensory information often involves the activity of neuronal populations. We demonstrate how the accuracy of a population code depends on integration time, the size of the population, and noise correlation between the participating neurons. The population we study consists of 10 identified visual interneurons in the blowfly Calliphora vicina involved in optic flow processing. These neurons are assumed to encode the animal's head or body rotations around horizontal axes by means of graded potential changes. From electrophysiological experiments we obtain parameters for modeling the neurons' responses. From applying a Bayesian analysis on the modeled population response we draw three major conclusions. First, integration of neuronal activities over a time period of only 5 ms after response onset is sufficient to decode accurately the rotation axis. Second, noise correlation between neurons has only little impact on the population's performance. And third, although a population of only two neurons would be sufficient to encode any horizontal rotation axis, the population of 10 vertical system neurons is advantageous if the available integration time is short. For the fly, short integration times to decode neuronal responses are important when controlling rapid flight maneuvers.

scholarly journals Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Alexandre Mahrach ◽  
Guang Chen ◽  
Nuo Li ◽  
Carl van Vreeswijk ◽  
David Hansel
Keyword(s):  
Motor Cortex ◽  
Numerical Simulations ◽  
Barrel Cortex ◽  
Population Models ◽  
Gabaergic Interneurons ◽  
Paradoxical Effect ◽  
Form Complex ◽  
Recurrent Excitation ◽  
Layer 2

GABAergic interneurons can be subdivided into three subclasses: parvalbumin positive (PV), somatostatin positive (SOM) and serotonin positive neurons. With principal cells (PCs) they form complex networks. We examine PCs and PV responses in mouse anterior lateral motor cortex (ALM) and barrel cortex (S1) upon PV photostimulation in vivo. In ALM layer five and S1, the PV response is paradoxical: photoexcitation reduces their activity. This is not the case in ALM layer 2/3. We combine analytical calculations and numerical simulations to investigate how these results constrain the architecture. Two-population models cannot explain the results. Four-population networks with V1-like architecture account for the data in ALM layer 2/3 and layer 5. Our data in S1 can be explained if SOM neurons receive inputs only from PCs and PV neurons. In both four-population models, the paradoxical effect implies not too strong recurrent excitation. It is not evidence for stabilization by inhibition.

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